nolancacheux/Rag-Equity-Research-Agent
🤖 AI Agentnolancacheux
An autonomous AI agent for multi-source equity research, combining hybrid RAG with real-time financial data analysis.
The Rag-Equity-Research-Agent is a sophisticated framework designed to automate the labor-intensive process of equity analysis. At its core, it uses LangGraph to manage complex, multi-step reasoning workflows required for financial research. The agent employs a hybrid RAG (Retrieval-Augmented Generation) architecture to ensure that both structured financial data and unstructured sentiment from sources like Reddit or news feeds are synthesized accurately.
Key technical features include integration with Azure OpenAI for high-quality report generation and a modular FastAPI structure that allows for easy extension. The agent is built to handle multi-source ingestion, specifically targeting SEC 10-K filings for fundamental analysis and real-time market feeds for current pricing. By automating the collection and synthesis of these disparate data points, it significantly reduces the time required to produce comprehensive investment research reports. The project is container-ready, optimized for deployment on Azure Container Apps, and provides flexible interaction models through both a Telegram interface and a programmatic REST API.
💡Highlights
- ├─LangGraph-based orchestration
- ├─Hybrid RAG for multi-source data
- └─Azure-ready container deployment
🎯For
- ├─Financial Analysts
- ├─AI Engineers
- └─Fintech Developers